Method of Automatically Determining Sensor Placement in a Target Environment

Abstract

A method of automatically determining sensor placement in a target environment. The method comprises receiving as inputs: a three-dimensional (3D) map of the target environment; a number of a plurality of sensors to be placed in the target environment; a set of placement configuration parameters for the plurality of sensors; and a set of constraints for the plurality of sensors in the target environment. The method includes, based on the received inputs, using a simulation platform to simulate, based on one or more defined conditions in the target environment, operation of the plurality of sensors to generate a dataset comprising simulated output data for the plurality of sensors; and using a Reinforcement Learning (RL) algorithm to determine from the dataset comprising simulated output data for the plurality of sensors an optimized or at least an improved set of placement configuration parameters for the plurality of sensors.

1 . A method of automatically determining sensor placement in a target environment, the method comprising:
 receiving as inputs: a three-dimensional (3D) map of the target environment; a number of a plurality of sensors to be placed in the target environment; a set of placement configuration parameters for the plurality of sensors; and a set of constraints for the plurality of sensors in the target environment;   based on the received inputs, using a simulation platform to simulate, based on one or more defined conditions in the target environment, operation of the plurality of sensors to generate a dataset comprising simulated output data for the plurality of sensors; and   using a reinforcement learning (RL) algorithm to determine from the dataset comprising simulated output data for the plurality of sensors an optimized or at least an improved set of placement configuration parameters for the plurality of sensors.   
     
     
         2 . The method of  claim 1 , wherein the method comprises:
 using the simulation platform to generate respective datasets for a plurality of target environment scenarios; and   using the respective datasets to train the RL algorithm to determine optimized or at least improved sets of placement configuration parameters for the plurality of sensors for the plurality of target environment scenarios.   
     
     
         3 . The method of  claim 2 , wherein one of the received inputs includes an optimized or at least an improved set of placement configuration parameters for one of the plurality of target environment scenarios. 
     
     
         4 . The method of  claim 2 , wherein the method comprises using data generated for the respective datasets for the plurality of target environment scenarios to dynamically adapt to new or different target environment scenarios or scenes. 
     
     
         5 . The method of  claim 1 , wherein the method is applied iteratively for a plurality of adjustments to the user inputted data to thereby determine an optimized or at least an improved set of placement configuration parameters for the plurality of sensors. 
     
     
         6 . The method of  claim 1 , wherein the method is applied iteratively for a plurality of sensor model types, the sensor model types having different values for the constraints in the target environment, to thereby determine an optimized or at least an improved set of placement configuration parameters for the plurality of sensors including sensor model type. 
     
     
         7 . The method of  claim 1  wherein a reward parameter for the RL algorithm comprises simulated coverage area for each of the plurality of sensors. 
     
     
         8 . The method of  claim 7 , wherein the method comprises maximizing sensor scan coverage areas to converge towards optimal sensor placement but taking into account a hybrid objective comprising any one or more of: budget for sensor placement; sensor specification(s); constraints on sensor placement; any defined conditions for the target environment. 
     
     
         9 . The method of  claim 7 , wherein the simulated coverage area for each of the plurality of sensors comprises simulated 3D point cloud data for each of the plurality of sensors. 
     
     
         10 . The method of  claim 9 , wherein the simulated 3D point cloud data for each of the plurality of sensors is approximated based on point distance, point cloud density, and point distribution uniformity. 
     
     
         11 . The method of  claim 10 , wherein the 3D point cloud data for each of the plurality of sensors is approximated by:
 representing the 3D point cloud data as a 2D grid; and   applying the L1 Norm (Manhattan Distance) to each data point in 2D grid.   
     
     
         12 . The method of  claim 11 , wherein the method comprises:
 dividing the 2D grid into rectangular boxes;   assigning “x” and “y” coordinates to each data point in the rectangular boxes; and   applying the L1 Norm (Manhattan Distance) to each data point in the rectangular boxes.   
     
     
         13 . The method of  claim 1 , wherein the method comprises, in one or more iterations, reducing the number of a plurality of sensors to be placed in the target environment and determine an optimized or at least an improved set of placement configuration parameters for the reduced number of sensors. 
     
     
         14 . The method of  claim 1 , wherein the method comprises inputting the set of placement configuration parameters for the plurality of sensors as an initial set of placement configuration parameters and iteratively implementing the method for respective adjustments of one or more of the placement configuration parameters to determine an optimized or at least an improved set of placement configuration parameters for the plurality of sensors. 
     
     
         15 . The method of  claim 14 , wherein the initial set of placement configuration parameters are selected to provide a semi-optimal direction for the RL model where said initial set of placement configuration parameters are mathematically calculated using trigonometry from data defining the target environment or a target environment scenario. 
     
     
         16 . The method of  claim 1 , wherein one or more weighting values applied to one or more of the constraints in the set of constraints for the plurality of sensors in the target environment. 
     
     
         17 . The method of  claim 1 , wherein the plurality of sensors comprises light detection and ranging (LiDAR) sensors and the target environment comprises a road traffic environment. 
     
     
         18 . The method of  claim 1 , wherein the simulation platform comprises any of: a CARLA simulator for autonomous driving research; an Autoware (Gazebo) simulator; an Airsim (UE4 & Unity) simulator; or a TORCS (Open GL) simulator. 
     
     
         19 . The method of  claim 1 , wherein the RL algorithm comprises a universal RL model. 
     
     
         20 . An apparatus for automatically determining roadside sensor placement in a target environment, the apparatus comprising a memory for storing machine-readable instructions and a processor for executing said machine-readable instructions configuring the processor to implement the steps of:
 receiving as inputs: a three-dimensional (3D) map of the target environment; a number of a plurality of sensors to be placed in the target environment; a set of placement configuration parameters for the plurality of sensors; and a set of constraints for the plurality of sensors in the target environment;   based on the received inputs, using a simulation platform to simulate, based on one or more defined conditions in the target environment, operation of the plurality of sensors to generate a dataset comprising simulated output data for the plurality of sensors; and   using a Reinforcement Learning (RL) algorithm to determine from the dataset comprising simulated output data for the plurality of sensors an optimized or at least an improved set of placement configuration parameters for the plurality of sensors.   
     
     
         21 . A non-transitory computer readable medium comprising machine-readable instructions which, when executed by a processor, causes the processor to implement the steps:
 receiving as inputs: a three-dimensional (3D) map of the target environment; a number of a plurality of sensors to be placed in the target environment; a set of placement configuration parameters for the plurality of sensors; and a set of constraints for the plurality of sensors in the target environment;   based on the received inputs, using a simulation platform to simulate, based on one or more defined conditions in the target environment, operation of the plurality of sensors to generate a dataset comprising simulated output data for the plurality of sensors; and   using a Reinforcement Learning (RL) algorithm to determine from the dataset comprising simulated output data for the plurality of sensors an optimized or at least an improved set of placement configuration parameters for the plurality of sensors.